PartAI/TookaBERT-Base

Model Details

TookaBERT models are a family of encoder models trained on Persian in two sizes base and large. These Models pre-trained on over 500GB of Persian data including a variety of topics such as News, Blogs, Forums, Books, etc. They pre-trained with the MLM (WWM) objective using two context lengths.

For more information you can read our paper on arXiv.

How to use

You can use this model directly for Masked Language Modeling using the provided code below.

from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("PartAI/TookaBERT-Base")
model = AutoModelForMaskedLM.from_pretrained("PartAI/TookaBERT-Base")

# prepare input
text = "شهر برلین در کشور <mask> واقع شده است."
encoded_input = tokenizer(text, return_tensors='pt')

# forward pass
output = model(**encoded_input)

It is also possible to use inference pipelines such as below.

from transformers import pipeline

inference_pipeline = pipeline('fill-mask', model="PartAI/TookaBERT-Base")
inference_pipeline("شهر برلین در کشور <mask> واقع شده است.")

You can use this model to fine-tune it over your dataset and prepare it for your task.

• DeepSentiPers (Sentiment Analysis)
• ParsiNLU - Multiple-choice (Multiple-choice)

Evaluation

TookaBERT models are evaluated on a wide range of NLP downstream tasks, such as Sentiment Analysis (SA), Text Classification, Multiple-choice, Question Answering, and Named Entity Recognition (NER). Here are some key performance results:

Model name	DeepSentiPers (f1/acc)	MultiCoNER-v2 (f1/acc)	PQuAD (best_exact/best_f1/HasAns_exact/HasAns_f1)	FarsTail (f1/acc)	ParsiNLU-Multiple-choice (f1/acc)	ParsiNLU-Reading-comprehension (exact/f1)	ParsiNLU-QQP (f1/acc)
TookaBERT-large	85.66/85.78	69.69/94.07	75.56/88.06/70.24/87.83	89.71/89.72	36.13/35.97	33.6/60.5	82.72/82.63
TookaBERT-base	83.93/83.93	66.23/93.3	73.18/85.71/68.29/85.94	83.26/83.41	33.6/33.81	20.8/42.52	81.33/81.29
Shiraz	81.17/81.08	59.1/92.83	65.96/81.25/59.63/81.31	77.76/77.75	34.73/34.53	17.6/39.61	79.68/79.51
ParsBERT	80.22/80.23	64.91/93.23	71.41/84.21/66.29/84.57	80.89/80.94	35.34/35.25	20/39.58	80.15/80.07
XLM-V-base	83.43/83.36	58.83/92.23	73.26/85.69/68.21/85.56	81.1/81.2	35.28/35.25	8/26.66	80.1/79.96
XLM-RoBERTa-base	83.99/84.07	60.38/92.49	73.72/86.24/68.16/85.8	82.0/81.98	32.4/32.37	20.0/40.43	79.14/78.95
FaBERT	82.68/82.65	63.89/93.01	72.57/85.39/67.16/85.31	83.69/83.67	32.47/32.37	27.2/48.42	82.34/82.29
mBERT	78.57/78.66	60.31/92.54	71.79/84.68/65.89/83.99	82.69/82.82	33.41/33.09	27.2/42.18	79.19/79.29
AriaBERT	80.51/80.51	60.98/92.45	68.09/81.23/62.12/80.94	74.47/74.43	30.75/30.94	14.4/35.48	79.09/78.84

*Note because of the randomness in the fine-tuning process, results with less than 1% differences are considered together.

Contact us

If you have any questions regarding this model, you can reach us via the community of the model in Hugging Face.